Estimating flow, resistance or pressure from pressure or flow measurements and antiography

ABSTRACT

Systems and related methods to estimate, for a liquid dynamic system, flow or resistance based on a model of an object and pressure measurements collected in-situ at said object. Alternatively, pressure flow measurements are collected and pressure or resistance is being estimated.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/764,878, filed Mar. 29, 2018, now U.S. Pat. No. 11,177,030, which isthe U.S. National Phase application under 35 U.S.C. § 371 ofInternational Application No. PCT/EP2016/072885, filed on Sep. 27, 2016,which claims the benefit of European Patent Application No. 15187350.2,filed on Sep. 29, 2015. These applications are hereby incorporated byreference herein.

FIELD OF THE INVENTION

The invention relates to estimation systems for fluid dynamics, toestimation methods, to a computer readable media, and to computerprogram elements.

BACKGROUND OF THE INVENTION

Invasive catheter-based velocity measurement techniques have recentlyreceived increasing attention in functional stenosis assessment (e.g.,in coronary arteries), especially in combination with pressuremeasurements by so-called “Combo-wires”. These are guide wire systemsthat include measurement components such as intra-vascular ultrasoundtransducers for velocity measurements. There are also flow measurementtechniques based on temperature transducer readings.

However, these approaches may be considered unreliable, as flow can bemeasured only locally but flow varies strongly over a cross-sectionalarea.

Other methods for flow quantification include the use of PET (positronemission tomography) such as reported K L Gould et al in “Anatomicversus physiologic assessment of coronary artery disease: role ofcoronary flow reserve, fractional flow reserve, and positron emissiontomography imaging in revascularization decision-making”, Journal of theAmerican College of Cardiology 62, 18 (2013), pp. 1639-1653. Yet anotherapproach is based on angiographic densitometry as described for instanceby S Molloi et al in “Estimation of coronary artery hyperemic blood flowbased on arterial lumen volume using angiographic images”, InternationalJournal of Cardiovascular Imaging 28 (2012), pp. 1-11. But thesetechniques tend to be complicated (because of the need for systemcalibration and approximating scaling laws in case of the method by SMolloi) or expensive, or are not readily available.

SUMMARY OF THE INVENTION

There may therefore be a need for an alternative system or method toestimate flow or pressure or resistance in fluid dynamic systems.

The object of the present invention is solved by the subject matter ofthe independent claims where further embodiments are incorporated in thedependent claims. It should be noted that the following described aspectof the invention equally applies to the estimation methods, to thecomputer program elements and to the computer readable media.

According to a first aspect of the invention there is provided anestimation system for fluid dynamics, comprising:

an input port for receiving

i) at least one fluid pressure measurement collected in-situ from avessel tree, each pressure measurement being associated with a locationwithin the vessel tree, and

ii) medical image data of the vessel tree;

a model builder for generating a 3D geometrical model derived from themedical image data;

a registration unit for spatially registering the at least one fluidpressure measurement to the generated 3D geometrical model based on itsassociated location, and

a liquid dynamics analyzer configured to compute, based on the 3Dgeometrical model of the vessel tree, at least one flow and/orresistance value with respect to said vessel tree, using the spatiallyregistered pressure measurements as boundary conditions.

The step of computing the flow or resistance includes in particularusing the measured and spatially registered pressure readings asboundary conditions for solution in CFD algorithms.

We propose a novel approach to estimate blood flow and/or resistancebased on intra-vascular pressure measurements with a geometric modelderived from medical image data.

The proposed system and method can be used with benefit in and alongsidefractional flow reserve (FFR) contexts. FFR provides a way to assessfunctional stenosis severity. FFR is a reliable measure for grading thefunctional limitations induced by a stenosis. Based on the aorticpressure P_(a) and the pressure P_(d) distal to the stenosis, FFR isdefined as the ratio FFR=P_(d)/P_(a). FFR is a widely used index toassess the functional impact of a stenosis in the coronary arteries.Typically, FFR is measured in an invasive fashion, by advancing apressure wire past the stenosis and measuring the pressure drop acrossthe stenosis.

In one embodiment, the proposed system and method allows harnessing thepressure readings which are collected during a conventional FFRmeasurement anyway. The pressure measurements are combined with an imagebased (e.g., angiographic X-ray or computer tomography) assessment ofthe coronary vessel geometry. A computational model is used based onthis imagery to deliver a more complete picture of the geometry of thecoronary and/or myocardial condition, which FFR alone could not give. Inother words, instead of competing with FFR, the invention improves andadds up to FFR and provides in addition flow or resistance information.The use of dedicated and additional flow measurements is not necessary.For present purposes it is enough then to only measure pressure (and notflow) which can be done relatively cheaply because the pressuresensitive instrumentation is in general cheap or cheaper than flowsensitive instrumentation. Flow information is computed instead based oneasily obtainable in-situ pressure measurements. “Relatively cheap” asused herein is in relation to flow or combo-wires which are in generalmore expensive to build than mere pressure measurement device (alsoreferred to a “pressure wires”).

According to one embodiment, the model includes at least one locationthat represents a stricture in said object and wherein the model builderis reconfigured to modify the model to remove or a least mitigate saidstructure and wherein the fluid dynamics analyzer is configured isre-compute the at least one flow and/or resistance value based on themodified model. In other words, in this embodiment proposed system canbe used with benefit for virtual checks of future therapy effects tobetter understand their medical benefits. The object (e.g. vessel) canbe “virtually repaired” by geometrically removing a stricture structure(such a stenosis) from the model and then the computations are rerunbased on the modified model that now represents the vessel withmitigated stenosis or without stenosis. The benefits of the stenosistherapy can then be better assessed “virtually”, that is, beforehand. Inone embodiment of this virtual repair procedure, in any given branch ofthe vessel model, the readings downstream the (former) stenosis locationare either retained as boundary conditions in the computations, orignored as boundary conditions in the computations or, as a middleground between these two extremes, the respective downstream readingsare at least mitigated (be weighing) or modified for any stenosis thatthat one wishes to repair.

According one embodiment, the computations of flow or resistance can beextrapolated into branches of the vessel where no pressure measurementshave been collected to extend the remit of the proposed approach toother parts of the model, in particular then whole of the model.

In general more than one values (scalar or vectorial) are computed for aplurality of measurement locations. The values together definerespective spatial distribution which of the respective fluid dynamicalquantity (flow, pressure or resistance).

According to one embodiment, the system according to either one of thetwo aspects comprises a visualizer configured to render on a displaydevice a visualization of the computed flow, pressure or resistancedistribution.

According to one embodiment, said visualization comprises a spatiallyresolved flow map, representing the flow distribution in associationwith positions within said object.

According to one embodiment, the image data includes angiography data orcomputed tomography data. Alternatively or in addition, intra-vascularoptical coherence tomography data, MR data, or ultrasound data, inparticular intravascular ultrasound data, may be used. Preferably the atleast one fluid pressure measurement is collected during a fractionalflow reserve procedure.

According to one embodiment, the system comprises an imaging apparatusfor supplying said image data.

According to one embodiment, the system comprises a measurement devicefor introduction into the object for collecting the at least onepressure measurement inside the object. More particularly, said pressuremeasurement device is a catheter having at least one pressure sensor.

In a preferred embodiment, the catheter is provided at its head or tipwith a tracker including a location transducer. Thus, for each pressuremeasurement, a spatial location to be associated with the measurementmay be established with high accuracy. In accordance with the invention,this location data is used in registering the pressure measurements tothe geometrical vessel model.

According to second aspect there is provided an estimation method,comprising:

receiving at least one fluid pressure measurement collected in-situ froman object, each measurement being associated with a location within thevessel tree;

receiving medical image data;

generating a 3D geometrical model derived from the image data;

registering the at least one fluid pressure measurement to the generated3D geometrical model based on its associated location, and

computing, based on the 3D geometrical model of said vessel tree, atleast one flow and/or resistance value with respect to said vessel tree,using the spatially registered pressure measurements as boundaryconditions.

Applications of the proposed methods and systems are mainly envisaged inthe medical field, in particular cardiology. However that is not to saythat other applications are excluded herein. First, the systems andmethods may be used to estimate flow, pressure or resistance in otherorgans than the heart coronaries and the respective input measurementsmay be collected in other than FFR contexts. For instance, leg arteriesmay be analyzed. In a further extension, flow or pressure of urine inurological investigations such as in (VCMG) Videocystometrography may becomputed instead of blood flow. Second, it is also outside the medicalfield where the proposed systems and methods may be practiced withbenefit, for instance in geology such as in speleological exploration ofunderwater cave systems or other.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the invention will now be described withreference to the following drawings wherein:

FIG. 1 shows a flow or pressure estimation system;

FIG. 2 shows a graphics display generated by the system in FIG. 1;

FIG. 3 shows a flow chart of a flow and pressure estimation methods;

FIG. 4 shows an illustration of flow and resistance method; and

FIG. 5 is an illustration of a part of a vessel model.

DETAILED DESCRIPTION OF EMBODIMENTS

With reference to the schematic block diagram in FIG. 1 components ofliquid dynamic analyzer system are shown.

The system can be used for volumetric flow or resistance estimation inan FFR context but applications other than in FFR, indeed even otherthan medical, are not excluded herein.

Briefly, and according to one embodiment the approach proposed hereincan be used for blood flow measurement which combines intra-vascularpressure measurements with a geometric model of the vessel underinvestigation which is derived from medical image data. Advantageously,but not necessarily in all embodiments, the pressure measurements arethose collected during a conventional FFR intervention with a suitablemeasurement device MD.

In more detail and with continued reference to FIG. 1, an imagingmodality IM is used to acquire preferably, but not necessarily, 3D imagedata of an object of interest, for instance, of the coronary orperiphery vessels COR of a human or animal patient. In one embodiment,the image data is generated in a rotational angiographic imaging run.Before, during or after the imaging, the measurement device MD such as apressure wire is used to acquire in an interventional procedure, at aplurality of locations within or at the vessel system COR, respectivepressure readings. The pressure wire is a steerable guide wire with ameasurement head. The measurement head is formed from one or morepressure transducers/sensors mounted proximal to a tip portion of theguide wire MD. The pressure wire may also include a torque device tofacilitate navigation through the vasculature. The wire MD may alsoinclude a tracking (sub-)system TR such as optical (shape sensing)electromagnetic tracking or the wire MD may be tracked in 3D using imageprocessing of the supplied imagery and knowledge of the imaging geometryused by the imager IM.

Preferably, the readings include those collected across a stenosis inthe vessel tree. For example, this may be done in a pull-back-sequenceprotocol during an FFR invasion. It will be understood that eachpressure reading is in general associated or at least associable with acertain spatial location X_(i) within the coronary COR at which therespective reading is acquired. This pressure-versus-locationassociation is either generated automatically for instance using alocation transponder of the tracker TR coupled to the head of thepressure wire. Alternatively, the locations can be computed from a giveninitial position of the measurement head and a given spatial samplingfrequency (that is, how many measurements are taken per unit length) anda known measurement trajectory (which is indeed known for instance in apullback measurement sequence).

The pressure readings and the image data are then forwarded as input toa processor section PC which can be implemented as a software module ona general purpose computer such as a workstation WS. Based on thisinput, the processor component of the proposed liquid dynamics analyzersystem produces in one embodiment the desired volumetric blood flowand/or resistance data for the vessel system COR under examination.

The processing section PC receives the image data and the pressurereadings at input port IN. From the image data, a 3D geometrical model(such as a mesh model, etc.) is generated by a model builder MB. Thiscan be achieved in one embodiment by vessel segmentation, that is,segmentation based on image element (pixel, voxel) intensity.Alternatively, a 2D vessel angiogram can be segmented and based on the2D curvature of the vessel, the projection geometry used by the imagingmodality IM for the acquisition of the angiogram and the assumption ofspherical vessel geometry a 3D vessel model is constructed. In otherword, this allows building a 3D model from a single projection image.The model so generated and the pressure readings are then spatiallyregistered onto each other by a registration unit (not shown). In otherwords, in the registration, respective locations associated with thepressure readings are made to correspond to respective geometricalpoints in the model. Registration can be either image basedregistration, manual registration or be may be based on the trackingdata supplied by the tracker TR, if any.

The spatially registered readings are then forwarded to the liquiddynamics analyzer LDA component. This is arranged in one embodiment as aflow or resistance estimator which uses for instance computational flowdynamic (CFD) methods to compute volumetric flow values/estimates Q_(i).The computed values are either scalar or a vector field.

More particularly, the acquired pressure readings are used as boundaryconditions to compute the spatially resolved flow distribution. Whilstthe model forms the geometrical constraints for the flow, pressuredreadings describe the flow locally.

The relationship between volumetric flow and pressure is known to begoverned by a system of partial differential equations such asNavier-Stokes equation or approximation thereof (e.g., a lumpedparameter model). The type of equations is encoded into the flowestimator FE. The partial differential equations are spatiallydiscretized by finite element methods into a potentially large set ofordinary differential equations which can then be then solved by variousnumerical techniques for the flow values. Solutions to the CFD problemcan be generally described as vector fields (p,{right arrow over (Q)})(p a position and {right arrow over (Q)} being a velocity vector for theflow at that point). The vector fields are a collection of function andthe boundary conditions prescribe through which points in phase spaceany possible solution must pass. That is, for any solution (p,{rightarrow over (Q)}), the pressure at p must equal the pressure value Pcollected at said point p. The underlying CFD algorithm takes theseconstraints imposed by the boundary conditions into account.

If one is interested only in the magnitude (speed) of the velocityvectors as per the computed vector field solution, one can convertedinto a scalar field by taking absolute values of the vector component(p, |{right arrow over (Q)})|=Q). Alternatively, using the lumpedparameter model or other approaches one can compute the values directlyas scalars.

In another embodiment, a lumped elements approach is described forinstance in US 2011/0071404. The lumped elements approach has been shownto afford a quick turnaround. Any other CFD technique is also envisageherein. In one embodiment, the collection of the so computed volumetricflow values (in particular the speed at each point) together form aspatially resolved volumetric flow distribution for the model. That is,at each location of the model (which in turn corresponds to locationwithin the object COR), the flow estimator unit associates a flow value(volume per second, such ml/s for instance).

The so estimated flow or resistance values are produced at an outputport OUT as numerical values. Preferably the estimates are visualized bya visualizer VIZ and rendered for display on a monitor MT. Moreparticularly, the flow estimates are displaced spatially resolved, thatis, each estimate is shown associated with the respective location inthe model or in the image data.

FIG. 2 shows in panel B) an example of a visualization GD of a spatiallyresolved flow estimate map based on pressure measurements p1, p2, p3collected at different locations within the vessel COR which are showndiagrammatically in panel A). The visualization GD is formed fromspatially arranged flow estimates Q1, Q2 and Q3 computed by theestimator FE. The flow estimate values Q_(i) can be color-coded (shownin different line types: dotted, solid and dashed) and overlaid as shownin FIG. 2B) on a reproduction of the image data or of the model. Thegraphics display in 2B) illustrates as an example a rather coursediscretization with only 3 measurement collections. Therefore, the modelis broken up into three parts. More refined discretizations are alsoenvisaged but in principle it would be sufficient to sample for merely 3or even merely 2 pressure measurements.

It should be noted that the model builder MB may not necessarily operateto build the model from the image data itself. In an alternativeapproach a generic mesh model can be used which is then adaptedaccording to the image data acquired from the object. For instance,iterative forward-projection-techniques can be used to deform thegeneric in a series iteration until the projections across the deformedgeneric model corresponds to the acquired image data (real projections).In this manner a generic model can be personalized or “tailored” to theimage data of the object COR at hand.

To obtain more persuasive results in FFR it is advisable to acquirepressure readings in two states of the subject, one set in a state ofstress and one set in a relaxed state. In other words, at some or eachof the locations a pair of pressure readings are acquired: one in astressed state and the other in a relaxed state of the subject.

In one embodiment wherein the model builder MB is reconfigured to modifythe model to remove or a least mitigate a structural feature of themodel and to then said structure (e.g. stricture such as in stenosis)and wherein the liquid dynamics analyzer LDA is configured is re-computethe flow value and/or resistance value based on the modified model. Thisoperation will be explained in more detail below at steps S40 a,b.

With reference to FIG. 3 A), there is shown a flow chart of a liquiddynamics analyzing method, in particular a flow or resistance estimationmethod.

At step S10 a a plurality of pressure measurements are collected in situfrom or in an object at a plurality of locations. Multipleintra-vascular pressure measurements are taken using a catheter orguide-wire equipped with a pressure sensor. It has been shown to bebeneficial that that at least some of the measurement points orlocations (at which the pressure readings are collected in step S10 a)include locations of at least one inlet and an at least one outlet ofthe geometric vessel model of the vessel tree. This allows making thecomputations in the follows up (step S20 a) more realistic, even forother vessel segments in the tree. In addition thereto, in oneembodiment, a respective pair of measurements are placed up anddownstream any at least one or better still at any stenoses location.

At an optional step S20 a, flow distribution for a liquid residing in orpassing through the object is computed based on a model of said objectand based on using the collected measurements as boundary conditions ina CFD solving algorithm.

It is also envisaged in some embodiments to extrapolate flow orresistance values into parts of the model that correspond to location(in a branch of vessel mode for instance) in the object where nomeasurements have been collected. This is done by using one or more ofthe boundary conditions also for parts of the model in respect of whichno measurements have been collected. This can be done by using some ofthe boundary conditions at locations in said other parts that correspondstructurally or functionally to the locations in the measured branch.For instance a boundary condition that is based on a measurement at anoutlet point may be used at an outlet point in a different branch.

At (optional) step S30 a the estimated values or the flow distributionis visualized on a display device.

Although in the above embodiment in-situ pressure measurements have beencollected to compute volumetric flow values, a dual method to this isalso envisaged and shown in flow chart B) with corresponding steps S10b-S30 b. So, rather than collecting pressure measurement values in situ,it is flow measurement values that are collected in situ at step S10 b,and a pressure distribution is then computed completely analogous tostep S20 a in step S20 b. The pressure values can then be displayed asspatially resolved pressure distribution values at step S30 b.

In either one of the above methods, A) and B), at least the positions ofthe measurements with respect to the 3D geometric model should be known.The model can be constructed from image data acquired from theobject. 1. The image data is preferably 3D or stereoscopic. In case ofX-ray angiography, a rotational sequence or two projections fromdifferent angles might be used. Possibly a single projection might alsosuffice when one uses assumption on cross-sectional shapes of thevessels. For instance, one may assume a priori circular vesselcross-sections. In some embodiments, a construction of a mere 2D modelrather than a 3D model may also be sufficient. Another embodiment thatcan be based on uni-directional image data are densitometric methodswhere one predicts diameter in projection direction. See for instance US2007/0053558A1 on densitometry. In principle different imaging methodsthan X-ray angiography can be used for generating the geometric vesselmodel. This applies especially to intra-vascular optical coherencetomography (OCT) or ultrasound (US) but might also include MRT and CT.

In the flow estimation embodiment, the volumetric flow (measured involume/sec) values are computed as opposed to “point” speed values(measured in distance/length), the latter being usually produced byconventional flow measurement devices such as a combo-wire whichmeasures both, flow and pressure. Point speed values which can beconverted into volumetric data by multiplication with the respectivevessel cross-section as per the geometry of the model at the location atwhich the flow measurement was collected.

Whereas flow velocity asks with which velocity (direction and speed) animaginary point travels when suspended in the liquid under examination,volumetric flow asks for the amount of liquid that passed through animaginary planar region positioned at any given location in the liquid.Volumetric flow data has been found to be more relevant for assessingtissue viability than flow in terms of velocity and more relevant thanpressure.

The proposed method is especially sensitive in the presence of stenoses(when pressure drop from inlet to outlet occurs). However, it has beenfound less sensitive if the pressure drop is close to zero. Pressuremeasurement can be performed under normal conditions or duringdrug-induced hyperemia to increase the pressure loss along the vessel.

The proposed method and system may furnish a more reliable approach tovolumetric flow measurement and can potentially replace expensive flowmeasurement with “ComboWire” devices or PET measurements. “Combo-wires”include those measurement devices that allow measurement of both,pressure and flow.

A further refinement of the above proposed embodiments is to segment theimage data into perfused sub-volumes for each branch of the vessel treeand to assign a calculated blood flow to each sub-volume. This canprovide a “virtual perfusion map”.

When coronary pressure measurements are taken under normal conditionsand at hyperemia (i.e., in a relaxed state), the coronary flow reserve(CFR) index can be predicted from the calculated flow values. See forinstance Kern, Morton J et al in “Current concepts of integratedcoronary physiology in the catheterization laboratory”, Journal of theAmerican College of Cardiology 55, 3 (2010), pp. 173-185.)

In a yet further refinement, the pressure or flow measurements can besynchronized with an ECG signal, to so compute cardiac-phase-dependentflow or pressure, respectively. This may include utilizing a 4D (thatis, 3D+time) coronary model. FIG. 4 is an illustration of the method asper A), FIG. 3. Pressure readings P1-P7 are collected at differentlocations. Based on a 3D model and using the measured pressure data asboundary conditions in a CFD algorithm, corresponding flow values Q1-Q5are computed and, in addition, using the geometry of the model, flowQ6-Q8 can be extrapolated also into branches where no pressuremeasurement was collected. Alternatively and in a similar mannercorresponding resistance values R1-R5 can be computed for a measuredbranch and interpolated values R6, R7 for other branches (where nomeasurements have been collected). For instance, as can be seen in FIG.4 one extrapolates into other branches of the vessel model by using thereading P6 at the outlet of the measured branch as respective outletreading boundary conditions for the other (non-measured) branches to socompute values Q6-Q8 and R7-R8, respectively.

The same procedure as described above in relation to FIG. 4 can becarried out analogously for method B) in FIG. 3 where flow measurementsare collected and it is the pressure and/or resistance values that arecomputed (for an illustration of this embodiment, each “P” is exchangedfor a “Q” in FIG. 4).

Yet another refinement combinable with any of the above described isillustrated in FIG. 5. The boundary conditions (that is, the pressure orflow measurements collected in situ) can be localized down tocross-sectional level as shown in the Figure, given the trackinginformation supplied by the tracker sub-system TR is detailed enough. Sorather than merely assigning the boundary collection to a certainsection in the vessel (which may be fine in some embodiments), theboundary condition is assigned along a certain radius in the thatsection to precisely “peg” the boundary condition at an appropriatedistance from the vessel's wall in the respective vessel section. Theboundary conditions can then be mapped to spatial nodes as per the CFDalgorithms used. This refinement is particular advantageous in theembodiment of FIG. 3 B) where flow measurements are collected becausethese are known to vary along the cross-sectional radius, that is, withdistance from the vessel wall. These locations are shown in FIG. 5 as‘X’s in the shown exemplary cross section. For instance some 3D CFDalgorithms use a 3D node system made up from a plurality of elementssuch as a tetrahedron or others that cover the space within a vessel.The spatial assignment, within the model's cross-section, of themeasurements as boundary conditions to vertices or center points etc. ofthese node elements can be achieved by using the tracking information.The computations of the associated pressures can be expected to be moreaccurate. Also, these localization of boundary conditions can beexpected to return more accurate results for the FIG. 3 A) embodiment aswell.

It would also be possible to “repair” the stenotic vessel with QCA(Quantitative Coronary Analysis), i.e. estimating the “healthy” vesselcontour and redo the calculations, so that relative volume flows orpressures can be simulated. In one embodiment, in either one of theabove methods A),B) in FIG. 3, the method includes a further step forthe case where the model includes at least one location that representsa stricture such as a stenosis in the vessel object. This further stepincludes modifying S40 a,b the model to remove or a least mitigate saidstructure and re-computing i) the at least one flow and/or resistancevalue or ii) the at least one pressure/and or resistance value,respectively, based on the modified model. In more detail, and referringto method A) in FIG. 3 for sake of definiteness, the method can be usedwith benefit for virtual checks of future therapy measure to betterunderstand their medical benefits. The object (e.g. vessel) can be“virtually repaired” by geometrically removing or mitigating a stenosisfrom the model and then the computations of the CFD algorithm are rerunbased on the modified model that represents the vessel with mitigatedstenosis or without stenosis. The benefits of the stenosis therapy canthen be better assessed “virtually”, that is, beforehand. In otherwords, and according to one embodiment, the model builder MB may be(re)used to adapt the model by geometrically removing the respectivestrictures representing the respective stenoses. In one embodiment thisis done by using the vessel cross-sections/width up and downstream thestricture to linearly (or higher-dimensionally) interpolate between thetwo to thereby geometrically eliminate the stricture. In this manner, anew, modified model is created that represents new geometrical boundaryconditions.

The previously computed measurements are either retained as boundaryconditions for the modified model or the reading(s) downstream/the nowmodified stenosis are ignored or are likewise adapted to mitigate theeffect of these downstream boundary conditions. For instance, theboundary conditions may be multiplied by suitable weighting factors toimplement this mitigation in the computations of the flow or resistancesfor the modified model. This virtual repair procedure can be also usedanalogously with the embodiments B) in FIG. 3 where pressure and/orresistances are being computed. Preferably, these adaptations of therespective boundary conditions are done downstream the (former ormodified) strictures for any given branch of the vessel model where thestricture is located.

The methods or systems proposed above themselves to the functionalassessment of all types of stenosis in the arteries of the human bodyincluding coronaries, iliac, femoral, brachial, and hepatic arteries aswell as the carotids.

The components of the flow or pressure estimation system as per FIG. 1may be arranged as separate modules in a distributed architecture andconnected in a suitable communication network.

The components may be arranged as dedicated FPGAs or as hardwiredstandalone chips.

The components, and in particular the liquid dynamics analyzer LDA maybe programmed in a suitable programming language such as C++ or Croutines. Alternatively, higher level scientific computing platformssuch as Matlab® or Simulink® may be used.

In another exemplary embodiment of the present invention, a computerprogram or a computer program element is provided that is characterizedby being adapted to execute the method steps of the method according toone of the preceding embodiments, on an appropriate system.

The computer program element might therefore be stored on a computerunit, which might also be part of an embodiment of the presentinvention. This computing unit may be adapted to perform or induce aperforming of the steps of the method described above. Moreover, it maybe adapted to operate the components of the above-described apparatus.The computing unit can be adapted to operate automatically and/or toexecute the orders of a user. A computer program may be loaded into aworking memory of a data processor. The data processor may thus beequipped to carry out the method of the invention.

This exemplary embodiment of the invention covers both, a computerprogram that right from the beginning uses the invention and a computerprogram that by means of an up-date turns an existing program into aprogram that uses the invention.

Further on, the computer program element might be able to provide allnecessary steps to fulfill the procedure of an exemplary embodiment ofthe method as described above.

According to a further exemplary embodiment of the present invention, acomputer readable medium, such as a CD-ROM, is presented wherein thecomputer readable medium has a computer program element stored on itwhich computer program element is described by the preceding section.

A computer program may be stored and/or distributed on a suitable medium(in particular, but not necessarily, a non-transitory medium), such asan optical storage medium or a solid-state medium supplied together withor as part of other hardware, but may also be distributed in otherforms, such as via the internet or other wired or wirelesstelecommunication systems.

However, the computer program may also be presented over a network likethe World Wide Web and can be downloaded into the working memory of adata processor from such a network. According to a further exemplaryembodiment of the present invention, a medium for making a computerprogram element available for downloading is provided, which computerprogram element is arranged to perform a method according to one of thepreviously described embodiments of the invention.

It has to be noted that embodiments of the invention are described withreference to different subject matters. In particular, some embodimentsare described with reference to method type claims whereas otherembodiments are described with reference to the device type claims.However, a person skilled in the art will gather from the above and thefollowing description that, unless otherwise notified, in addition toany combination of features belonging to one type of subject matter alsoany combination between features relating to different subject mattersis considered to be disclosed with this application. However, allfeatures can be combined providing synergetic effects that are more thanthe simple summation of the features.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive. Theinvention is not limited to the disclosed embodiments. Other variationsto the disclosed embodiments can be understood and effected by thoseskilled in the art in practicing a claimed invention, from a study ofthe drawings, the disclosure, and the dependent claims.

In the claims, the word “comprising” does not exclude other elements orsteps, and the indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items re-cited in the claims. The mere fact that certainmeasures are re-cited in mutually different dependent claims does notindicate that a combination of these measures cannot be used toadvantage. Any reference signs in the claims should not be construed aslimiting the scope.

1. A system, comprising: an intravascular catheter or guidewirecomprising a pressure sensor configured to collect a plurality ofpressure measurements in-situ from only a first branch of a vessel tree,wherein the vessel tree includes the first branch and a different,second branch; and a processor configured for communication with theintravascular catheter or guidewire, wherein the processor is configuredto: receive medical image data of the vessel tree; receive the pluralityof pressure measurements; generate a geometrical model based on themedical image data; perform spatial registration of the plurality ofpressure measurements to the geometrical model based on an associationbetween each of the plurality of pressure measurements and a locationwithin the first branch where the pressure sensor collected therespective pressure measurement; automatically set first boundaryconditions for the first branch based on the spatial registration;compute, based on the first boundary conditions and the geometricalmodel, at least one of a first flow value or a first resistance valuefor the first branch; extrapolate at least one of a second flow value ora second resistance value to the second branch; and output to a displayin communication with the processor: the medical imaging data; avisualization of the plurality of pressure measurements overlaid on thefirst branch in the medical imaging data; a visualization of at leastone of the first flow value or the first resistance value overlaid onthe first branch in the medical imaging data; and a visualization of atleast one of the second flow value or the second resistance valueoverlaid on the second branch in the medical imaging data,  wherein, toextrapolate, the processor is configured to: automatically set secondboundary conditions for the second branch based on the first boundaryconditions and the geometrical model; and compute, based on thegeometrical model and the second boundary conditions, at least one ofthe second flow value or the second resistance value for the secondbranch.
 2. The system of claim 1, wherein the geometrical model includesa stricture in the first branch, wherein the processor is configured toapply a weighting factor to one or more of the first boundary conditionsbased on the stricture to modify the geometrical model and simulate atherapy, and wherein the processor is configured to recompute at leastone of the first flow value or the first resistance value based on themodified geometrical model to evaluate a benefit of the therapy.
 3. Thesystem of claim 1, wherein the medical image data includes x-ray data orcomputed tomography data.
 4. The system of claim 1, wherein theprocessor is configured for communication with an imaging apparatusconfigured to collect the medical image data.
 5. The system of claim 1,wherein the intravascular catheter or the guidewire comprises a trackerconfigured to establish the location within the first branch where thepressure sensor collected the respective pressure measurement.
 6. Thesystem of claim 1, wherein the plurality of pressure measurements arecollected across a stenosis in the first branch.
 7. The system of claim1, wherein the plurality of pressure measurements are collected from atleast one of an inlet of the first branch or an outlet of the firstbranch.
 8. The system of claim 1, wherein the location corresponds to arespective position along the vessel tree and a respective radius withina cross-section of the vessel tree at the respective position.
 9. Thesystem of claim 1, wherein the first flow value comprises acardiac-phase dependent flow value.
 10. The system of claim 1, whereinthe geometrical model comprises a two-dimensional (2D) model or athree-dimensional (3D) model.